Predicting unconventional reservoir potential from wire-line logs: A correlation between compositional and geomechanical properties of the Duvernay shale play of western Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Unconventional reservoir performance is assessed and quantified via integration of compositional, rock fabric, and static mechanical property analyses that are routinely performed on drill core or cuttings. This approach has several limitations; for example, it can only be used where drill core and cuttings are available, and comprehensive analysis may be cost prohibitive at the full scale of a resource play. In this contribution, we propose a novel workflow that provides a robust correlation between compositional, mineralogical, and geomechanical properties of unconventional shale plays and wire-line log signature. Our approach enables the extrapolation of compositional and mechanical reservoir properties into areas in which drill core is lacking but wire-line logs are available. We illustrate our workflow using a case study from the Duvernay unconventional shale play in western-central Alberta (Canada). Our analysis reveals a high degree of correlation between core-measured mineral components and two wire-line logs: pulsed neutron spectroscopy (PNS) and spectral gamma ray (SGR). In particular, we show that PNS-derived calcium, aluminum, and silicon concentrations and SGR-derived thorium and potassium concentrations may be used to identify silica-rich, clay-rich, and carbonate-rich intervals, respectively, within the reservoir. We show that these intervals exhibit distinct mechanical properties, suggesting that they are also characterized by distinct hydraulic fracturing efficiency. Since well logs are generally more abundant than drill cores, our approach may prove critical in assessing predrill reservoir potential not only in the Duvernay but in similar unconventional plays worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".